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Replace 'ground truth' with 'labels' (ultralytics#1337)
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* Replace 'ground truth' with 'labels'

* Using torch... update
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@glenn-jocher
glenn-jocher authored Nov 10, 2020
1 parent 1c8464e commit 67bf9a9
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Showing 4 changed files with 19 additions and 19 deletions.
2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -87,7 +87,7 @@ To run inference on example images in `data/images`:
$ python detect.py --source data/images --weights yolov5s.pt --conf 0.25

Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, output='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16160MB)
Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)

Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s]

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10 changes: 5 additions & 5 deletions test.py
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Expand Up @@ -204,8 +204,8 @@ def test(data,

# Plot images
if plots and batch_i < 1:
f = save_dir / f'test_batch{batch_i}_gt.jpg' # filename
plot_images(img, targets, paths, str(f), names) # ground truth
f = save_dir / f'test_batch{batch_i}_labels.jpg' # filename
plot_images(img, targets, paths, str(f), names) # labels
f = save_dir / f'test_batch{batch_i}_pred.jpg'
plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions

Expand Down Expand Up @@ -250,9 +250,9 @@ def test(data,
from pycocotools.cocoeval import COCOeval

imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api
cocoDt = cocoGt.loadRes(str(file)) # initialize COCO pred api
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoAnno = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO annotations api
cocoPred = cocoAnno.loadRes(str(file)) # initialize COCO pred api
cocoEval = COCOeval(cocoAnno, cocoPred, 'bbox')
cocoEval.params.imgIds = imgIds # image IDs to evaluate
cocoEval.evaluate()
cocoEval.accumulate()
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10 changes: 5 additions & 5 deletions tutorial.ipynb
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Expand Up @@ -605,7 +605,7 @@
"output_type": "stream",
"text": [
"Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n",
"Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)\n",
"Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
"\n",
"Fusing layers... \n",
"Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients\n",
Expand Down Expand Up @@ -735,7 +735,7 @@
"output_type": "stream",
"text": [
"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_conf=False, save_dir='runs/test', save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
"Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)\n",
"Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n",
"100% 170M/170M [00:05<00:00, 32.2MB/s]\n",
Expand Down Expand Up @@ -921,7 +921,7 @@
{
"output_type": "stream",
"text": [
"Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)\n",
"Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
"\n",
"Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=10, logdir='runs/', multi_scale=False, name='', noautoanchor=False, nosave=True, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
"Start Tensorboard with \"tensorboard --logdir runs/\", view at http://localhost:6006/\n",
Expand Down Expand Up @@ -1040,7 +1040,7 @@
},
"source": [
"Image(filename='runs/train/exp0/train_batch0.jpg', width=800) # train batch 0 mosaics and labels\n",
"Image(filename='runs/train/exp0/test_batch0_gt.jpg', width=800) # test batch 0 ground truth\n",
"Image(filename='runs/train/exp0/test_batch0_labels.jpg', width=800) # test batch 0 labels\n",
"Image(filename='runs/train/exp0/test_batch0_pred.jpg', width=800) # test batch 0 predictions"
],
"execution_count": null,
Expand All @@ -1056,7 +1056,7 @@
"`train_batch0.jpg` train batch 0 mosaics and labels\n",
"\n",
"> <img src=\"https://user-images.githubusercontent.com/26833433/83667626-8c37fe00-a583-11ea-997b-0923fe59b29b.jpeg\" width=\"750\"> \n",
"`test_batch0_gt.jpg` shows test batch 0 ground truth\n",
"`test_batch0_labels.jpg` shows test batch 0 labels\n",
"\n",
"> <img src=\"https://user-images.githubusercontent.com/26833433/83667635-90641b80-a583-11ea-8075-606316cebb9c.jpeg\" width=\"750\"> \n",
"`test_batch0_pred.jpg` shows test batch 0 _predictions_\n"
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16 changes: 8 additions & 8 deletions utils/general.py
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Expand Up @@ -275,18 +275,18 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-re
ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
for ci, c in enumerate(unique_classes):
i = pred_cls == c
n_gt = (target_cls == c).sum() # Number of ground truth objects
n_p = i.sum() # Number of predicted objects
n_l = (target_cls == c).sum() # number of labels
n_p = i.sum() # number of predictions

if n_p == 0 or n_gt == 0:
if n_p == 0 or n_l == 0:
continue
else:
# Accumulate FPs and TPs
fpc = (1 - tp[i]).cumsum(0)
tpc = tp[i].cumsum(0)

# Recall
recall = tpc / (n_gt + 1e-16) # recall curve
recall = tpc / (n_l + 1e-16) # recall curve
r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0]) # r at pr_score, negative x, xp because xp decreases

# Precision
Expand Down Expand Up @@ -1076,8 +1076,8 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
image_targets = targets[targets[:, 0] == i]
boxes = xywh2xyxy(image_targets[:, 2:6]).T
classes = image_targets[:, 1].astype('int')
gt = image_targets.shape[1] == 6 # ground truth if no conf column
conf = None if gt else image_targets[:, 6] # check for confidence presence (gt vs pred)
labels = image_targets.shape[1] == 6 # labels if no conf column
conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred)

boxes[[0, 2]] *= w
boxes[[0, 2]] += block_x
Expand All @@ -1087,8 +1087,8 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
cls = int(classes[j])
color = color_lut[cls % len(color_lut)]
cls = names[cls] if names else cls
if gt or conf[j] > 0.3: # 0.3 conf thresh
label = '%s' % cls if gt else '%s %.1f' % (cls, conf[j])
if labels or conf[j] > 0.3: # 0.3 conf thresh
label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)

# Draw image filename labels
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